Cleanup

When developing for RunPod serverless, it's crucial to manage resources efficiently. The RunPod SDK provides a clean() function to help you remove temporary files and folders after processing. This guide will show you how to use this cleanup utility effectively.

The clean() Function

The clean() function is part of RunPod's serverless utilities. It helps maintain a clean environment by removing specified folders and files after a job is completed.

To use it, import the function from the RunPod serverless utilities:

from runpod.serverless.utils.rp_cleanup import clean

Default Behavior

By default, clean() removes the following:

input_objects folder
output_objects folder
job_files folder
output.zip file

Using clean() in Your Handler

Here's an example of how to incorporate the clean() function in your AI model handler:

Python

import runpod
from runpod.serverless.utils.rp_cleanup import clean
import requests
import os


def download_image(url, save_path):
    response = requests.get(url)
    if response.status_code == 200:
        with open(save_path, "wb") as file:
            file.write(response.content)
        return True
    return False


def handler(event):
    """
    This is a sample AI model handler function that downloads an image,
    processes it, and then cleans up.
    """
    try:
        # Extract the image URL from the input
        image_url = event["input"]["image_url"]

        # Create a temporary directory for the image
        os.makedirs("temp_images", exist_ok=True)
        image_path = "temp_images/downloaded_image.jpg"

        # Download the image
        if not download_image(image_url, image_path):
            raise Exception("Failed to download image")

        # Your AI model processing code here
        # For this example, we're just simulating processing
        result = f"Processed image from: {image_url}"

        # Cleanup after processing
        clean(folder_list=["temp_images"])

        # Return the result
        return {"output": result}
    except Exception as e:
        # If there's an error, attempt cleanup and return the error
        clean(folder_list=["temp_images"])
        return {"error": str(e)}


# Start the serverless function
runpod.serverless.start({"handler": handler})

In this example, clean() is called after the model processing is complete, ensuring that temporary files and folders are removed.

Custom Cleanup

You can also specify additional folders to be removed by passing a list to the clean() function:

clean(["custom_folder1", "custom_folder2"])

Testing your Handler with Cleanup

To test your handler with the cleanup function:

CLI
JSON

python ai_model_handler.py \
  --test_input '{
    "input": {
        "image_url": "https://avatars.githubusercontent.com/u/95939477?s=200&v=4"
    }
}'

Create a test_input.json file:

{
  "input": {
    "image_url": "https://avatars.githubusercontent.com/u/95939477?s=200&v=4"
  }
}

Then run:

python ai_model_handler.py

Best Practices

Call clean() at the end of your handler to ensure proper cleanup.
Use try-except blocks to handle any errors during cleanup.
Be cautious when adding custom folders to the cleanup list.
Consider logging cleanup actions for debugging purposes.

By implementing the clean() function in your handlers, you ensure that each job starts with a clean slate, preventing potential issues caused by leftover files from previous runs.

The clean() Function​

Default Behavior​

Using clean() in Your Handler​

Custom Cleanup​

Testing your Handler with Cleanup​

Best Practices​